In terms of sentence processing, there is known a technique for obtaining an expression of a word by using a vector of words co-occurring (appearing simultaneously) in a sentence. For example, the technique for preparing a cluster map by arranging clusters on a two-dimensional plane is known. This technique uses terminal equipment for a user which inputs a retrieval sentence or outputs a retrieval result, a retrieval device which performs retrieval processing of a patent document based on the retrieval sentence, and terminal equipment for management which registers the patent document in the retrieval device. In this technique, a large amount of technical documents (patent documents or the like) are efficiently classified into clusters on several multi-dimensional spaces, and those clusters are arranged on a two-dimensional plane so as to prepare a cluster map.
There is also known a technique for automatically determining semantic classification of context data obtained by a mobile device. In this technique, one or more context data streams are sampled with time, and a clustering algorithm is applied so as to identify one or more clusters in the sampled context data. Further, in this technique, a logic engine is run to automatically determine a concept name from a set of predefined concept names as a semantic classification of the one or more clusters, and the concept name is assigned to the one or more clusters or the assignment is suggested to the user.
Related arts are disclosed in Japanese Laid-open Patent Publication No. 2005-092442, Japanese Laid-open Patent Publication No. 2008-171418, Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, “Efficient Estimation of Word Representations in Vector Space.” Proceedings of Workshop at ICLR, 2013, Xu Chang et al., “RC-NET: A General Framework for Incorporating Knowledge into Word Representations.” Proceeding of the 23rd ACM International Conference on Conference on Information and Knowledge Management, ACM, 2014, Bengio, Yoshua, et al., “A Neural Probabilistic Language Model.” Journal of Machine Learning Research, 3. February, 1137-1155, 2003, and Guo, Jiang, et al., “Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources.” COLING, 2014, for example.